Brain Computer Interfaces (BCIs) are devices that allow the human brain to directly interact with technology via signals emitted from the skull. As with many such new technologies relating to the human brain and body, BCIs were first developed and used in laboratory, clinical, medical, and research settings. However, in recent years electroencephalography (EEG)-based BCI headsets have reached consumer-accessible prices, and are now being deployed in mobile applications, especially those focused on gaming and mental development. Medical applications of BCIs are beginning to include helping people with locked-in syndrome communicate; providing more autonomy to people with neuromuscular disorders; and helping with the rehabilitation of stroke survivors. Additionally, BCIs may aid in diagnosis and lead to preventative protocols for brain disorders, which are particularly important, in part due to increasing average life-expectancy, population growth, and number of people over the age of 65, i.e. Alzheimer's disease and other forms of age-related dementia are becoming an increasingly large problem. Furthermore, surveys consistently show that a large (and increasing) portion of the population suffers from some sort of mental illness: e.g. in 2010 approximately 1 in 3 Europeans met DSM-IV criteria for a mental or neurological disorder, see for example Wittchen et al. in “The Size and Burden of Mental Disorders and other Disorders of the Brain in Europe 2010” (European Neuropsychopharmacology, Vol. 21(9), pp. 655-679), in 2004 the rate was 1 in 4 in the United States, see for example Wittchen et al. in “Size and Burden of Mental Disorders in Europe—A Critical Review and Appraisal of 27 Studies” (European Neuropsychopharmacology, Vol. 15(4), pp 0.357-376).
Beyond medical applications, BCIs can provide members of the general public with insight into various aspects of their mental health, and act as tools for controlling/interacting with electronic systems. Additionally, EEG-based BCI devices can be used for neurofeedback, a series of techniques that give users the opportunity to train their brain by (among other things) increasing their ability to focus, reducing stress and anxiety levels, elevating mood, improving sleep, and enhancing cognitive processing and mental clarity. Also, neurofeedback can also be used as a treatment for a number of brain disorders, e.g. attention deficit hyperactivity disorder (ADHD), see for example Gevensleben et al. in “Is Neurofeedback an Efficacious Treatment for ADHD? A Randomised Controlled Clinical Trial.” (J. Child Psychology and Psychiatry, Vol. 50(7), pp. 780-789), and epilepsy, see for example Kotchoubey et al. in “Negative Potential Shifts and the Prediction of the Outcome of Neurofeedback Therapy in Epilepsy (Clinical Neurophysiology, Vol. 110(4), pp. 683-6).
Current BCIs range in complexity from medical/research-grade EEG devices with hundreds of sensors, to small headphone-like plastic headsets with only one or two. EEG headsets are designed for numerous purposes, but they typically fall into 2 categories: 1) medical/research headsets with a large number of sensors; and 2) simple devices with a small number of electrodes geared towards consumer devices and applications: e.g. games and general health and wellness software. Typically, medical/research headsets are bulky, stationary, uncomfortable, complex, user-unfriendly (and can thus only be operated by technicians and medical professionals), unattractive, and often require electrolyte solutions, glues, or gels for connectivity. Accordingly, it is beneficial to design sensors/electrodes/user interfaces with these issues in mind, i.e. a BCI that supports use for durations considerably longer than those of discrete medical visits and laboratory studies, but that still exhibits many of the most useful features of currently existing consumer and especially medical/research BCI headsets.
Electroencephalography (EEG) is a well-established technology that gathers information about what's going on inside a person's brain by recording signals produced by the firing of their neurons. When a neuron receives enough excitatory signals from sensory cells and other neurons, it produces a response called an action potential, which causes the neuron to release chemicals that excite all cells connected to a part of the firing neuron called the axon. During this process, there is a rapid exchange of ions (electrically-charged particles) that changes the voltage of the fluid surrounding the firing neuron in a predictable fashion. This voltage change then travels spherically outward from the firing neuron until it reaches the skull. EEG takes advantage of this to record brain activity by detecting voltages at one or more scalp locations over time (which alter in response to the firing of many neurons simultaneously), using electrodes attached to the surface of the head. Voltages are sampled from the electrodes at high frequencies—typically 1 kHz to 2 kHz—to provide an effectively continuous stream of data known as an EEG waveform. Spectral information is then extracted from this continuous stream, which results in discrete frequency-band ratios (wave types) generated from the raw data at frequencies in the ballpark of 100 Hz; for example, see Tatum et al in “Handbook of EEG Interpretation” (Demos Medical Publishing, 2008). These are generally divided into delta, theta, alpha, beta, mu, and gamma waves, with each type representing a specific range of frequencies. Some systems further subdivide these waveforms into subcategories, such as alpha1, alpha2, etc.—which essentially subdivide the frequency range of the entire waveform into smaller frequency ranges.
Past research has shown that different EEG waveforms correlate with activity in different regions of the brain, and thus with various internal mental states, for example particular emotions and thoughts, phases of sleep such as REM, and medically relevant neurological activity (e.g. seizures). Specific mental states can be identified by mathematically pre-processing the raw EEG data (e.g. using Fourier transforms; with various filters such as high-pass filters, low-pass filters, and bandpass filters; etc.), then applying algorithms that recognize EEG waveform features associated with a particular state—known as classification algorithms; see for example Shaker in “EEG Waves Classifier using Wavelet Transform and Fourier Transform” (Int. J. Biol. Life. Sci., Vol. 1, Iss. 2, p85-90). Algorithms exist for the quantification and tracking of mood, energy levels, epileptic seizures and seizure-like states, stages and quality of sleep, desire or craving for a particular object (e.g. a specific food), blinks, concentration/focus, relaxation/stress, and anxiety; see for example Rebolledo-Mendez et al in “Assessing Neurosky's Usability to Detect Attention Levels in an Assessment Exercise: (Human and Computer Interaction, pp 149-158, Springer-Verlag, 2009); and Crowley et al in “Evaluating a Brain-Computer Interface to Categorise Human Emotional Response” (IEEE 10th Int. Conf. Adv. Learning Technologies, pp 276-278). Accordingly, within the prior art EEG waveforms have been used to document a range of a user's neural processes and mental states.
This ability to externally read and record specific mental states led to neurofeedback: EEG-based treatments for neurological and/or mental disorders that use exercises developed to allow a person to alter these mental states directly (by manipulating its constituent waveforms). For example, knowledge of the EEG patterns correlated with attention—mainly beta waves—led to effective neurofeedback exercises for improving concentration, in which the feedback a user receives from the EEG analysis informs them of the extent to which they are focused. This allows users to purposefully induce these states by repeating the thought patterns they were engaged in when high levels of focus were reported by the EEG device. Increasing concentration in this manner actually strengthens the involved areas of the brain, such that the user sees improved focus in their day-to-day life, beyond the context of the exercise itself. This was demonstrated by research showing that 1) these areas of the brain observably grow, see for example Beauregard et al in “Functional Magnetic Resonance Imaging Investigation of the Effects of Neurofeedback Training on the Neural Bases of Selective Attention and Response Inhibition in Children with Attention-Deficit/Hyperactivity Disorder” (Appl. Psychophysiology and Biofeedback, Vol. 31, pp 3-20; and 2) it significantly improves symptoms of attention-deficit hyperactivity disorder (ADHD), a condition marked by a pathologically low attention span; see for example Arms et al in “Efficacy of Neurofeedback Treatment in ADHD: The Effects on Inattention, Impulsivity, and Hyperactivity: A Meta-Analysis” (Clinical EEG Neurosciences et al, Vol. 40(3), pp 180-189). This, however, is merely an example, as other research has demonstrated the efficacy of neurofeedback-related therapies for the treatment of conditions involving other mental states, such as depression using mood-elevating exercises and anxiety with neurofeedback-informed relaxation techniques, see for example Baehr et al in “Clinical Use of an Alpha Asymmetry Neurofeedback Protocol in the Treatment of Mood Disorders: Follow-Up Study One to Five Years Post Therapy” (J Neurotherapy, Vol. 4(4), pp 11-18) and Hammond in “Neurofeedback Treatment of Depression and Anxiety” (J Adult Dev., Vol. 12(2-3), pp 131-137).
Neurofeedback can also benefit people without mental or neurological disorders. Recent research has shown improvements in attention, semantic memory, and musical performance in healthy people after using neurofeedback exercises specifically designed to target each of those attributes. These performance enhancements were shown to translate into real-world settings; see for example Egner et al in “Ecological Validity of Neurofeedback: Modulation of Slow Wave EEG Enhances Musical Performance” (Neuroreport, Vol. 14, pp 1221-1224). Thus, neurofeedback can be used to cultivate desirable personal traits and improve quality of life, rather than simply to treat disorders. It is therefore a valuable tool for self-improvement that can allow healthy people to strengthen areas of cognitive and emotional weakness, and to improve their existing strengths.
Until recently, EEG measurement devices were expensive, bulky, stationary, and extremely difficult to use, and thus confined to a laboratory, clinic, or hospital setting. They were therefore only useful for research, diagnosis of various brain diseases, and for treatment of certain disorders in a clinical practice setting—which is very expensive. Accordingly, numerous potential treatments targeting neurological and mental disorders that require frequent, long-term, and self-administered EEG (IE a neurofeedback regiment that requires an extremely large number of sessions, which continue to be done intermittently near-indefinitely) were essentially impossible, as were all non-medical uses of EEG such as self-improvement or controlling video games. However, this has changed in recent years with the advent of small, inexpensive, and easy-to-use EEG headsets designed to be used by members of the general public rather than medical professionals and researchers. Typically, these consumer orientated EEG headsets exploit a small number of electrodes (e.g. 2-12), in contrast to the hundreds employed in medical and research systems. Such headsets are worn by the user throughout a neurofeedback activity, which typically last between a few minutes and an hour.
Early consumer EEG headsets were still relatively stationary, and coupled to fixed electronic devices (FEDs)—primarily desktop and laptop computers. However, due to the recent explosive market penetration of portable electronic devices (PEDs) such as personal digital assistants, smartphones, and tablet computers, this has begun to change. Certain consumer EEG devices have now been released that can interface with PEDs. Consumer EEG devices linked to PEDs have numerous advantages over consumer EEG devices that interface with FEDs. Such benefits include localized wireless interfacing, e.g. Bluetooth; portability. as EEG headsets interfaced to PEDs can be used essentially anywhere, and—since PEDs now outnumber FEDs—access to a larger market
Despite the great potential consumer EEG—especially PED-linked consumer EEG—has for medical, self-monitoring, and self-improvement applications, it is usually still treated as a novelty or toy, and used almost solely for entertainment purposes. Much of this has to do with limitations in development tools and supporting programs, which are geared primarily towards stationary use and games, which makes it difficult to create other types of software for consumer EEG. Also, these tools are geared toward developing applications intended for short-term use, and as such do not support numerous uses of consumer EEG applicable only when the devices are used for longer periods and/or continuously throughout the day, such as uses applicable to embedding EEG-based BCI into everyday activities or for the tracking of user medical information recorded throughout the day by EEG. Furthermore, existing development tools for consumer EEG are not conducive to web integration, which makes it nearly impossible to create consumer EEG applications that do such things as link EEG data to existing global databases, integrate EEG data with social media, send user EEG data to a dedicated server for deeper analysis than is practical on a smartphone, etc.
Another limitation in the prior art relating to neurofeedback and consumer EEG devices is the number of detectable and alterable mental states, which have mostly been confined to level of attention, relaxation, stress, and quality of sleep. Outside of academic research, the detection and alteration of states of mental clarity—which could otherwise be called level of mental fogginess, cognitive tempo, acute intelligence, mental “sharpness,” cognitive performance, or level of mental confusion—have been ignored. This state is associated with numerous forms of mental/cognitive processing and abstract thought, such as ability to reason and current level of creativity. Mental clarity is a detectable metric, as is strongly suggested by research on a phenomenon called “feature binding,” which is a person's ability to link information from different sources together to solve problems or be creative. On a simpler level, this can simply be the combination of 2 different types of sensory information coming in from different modalities to solve a small puzzle—for example figuring out what a particular object is when presented with a colour and a shape separately (e.g. the colour red is shown at one point, and a semi-circular shape later on, and the person is able to connect the separate pieces of information to determine that the object is an apple); see Keizer et al, 2010 in “Enhancing Cognitive Control Through Neurofeedback: A Role of Gamma-band Activity in Managing Episodic Retrieval” (Neuroimage, 49(4): p3404-3413)
Furthermore, the detection and alteration (through neurofeedback) of meditative states has primarily been treated as synonymous with relaxation, despite the numerous differences in the brain activity observed between relaxation and meditation. Meditative states also relate strongly to other detectable mental states—most notably level of attention. This fact has been largely ignored in algorithms used in previous embodiments of the invention.
According to embodiments of the invention the inventors have established new technologies and solutions that address these limitations within the prior art and provide benefits including, but not limited to, global acquisition and storage of acquired EEG data and processed EEG data, development interfaces for expansion and re-analysis of acquired EEG data, long-term/continuous user wearability, detection of states of mental clarity, and improved detection of states of meditation.
Other aspects and features of the present invention will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments of the invention in conjunction with the accompanying figures.